An Ethnographic Observational Study to Evaluate and Optimize the Use of Respiratory Acoustic Monitoring in Children Receiving Postoperative Opioid Infusions
Bibliographic record
Abstract
BACKGROUND: Respiratory depression in children receiving postoperative opioid infusions is a significant risk because of the interindividual variability in analgesic requirement. Detection of respiratory depression (or apnea) in these children may be improved with the introduction of automated acoustic respiratory rate (RR) monitoring. However, early detection of adverse events must be balanced with the risk of alarm fatigue. Our objective was to evaluate the use of acoustic RR monitoring in children receiving opioid infusions on a postsurgical ward and identify the causes of false alarm and optimal alarm thresholds. METHODS: A video ethnographic study was performed using an observational, mixed methods approach. After surgery, an acoustic RR sensor was placed on the participant's neck and attached to a Rad87 monitor. The monitor was networked with paging for alarms. Vital signs data and paging notification logs were obtained from the central monitoring system. Webcam videos of the participant, infusion pump, and Rad87 monitor were recorded, stored on a secure server, and subsequently analyzed by 2 research nurses to identify the cause of the alarm, response, and effectiveness. Alarms occurring within a 90-second window were grouped into a single-alarm response opportunity. RESULTS: Data from 49 patients (30 females) with median age 14 (range, 4.4-18.8) years were analyzed. The 896 bedside vital sign threshold alarms resulted in 160 alarm response opportunities (44 low RR, 74 high RR, and 42 low SpO2). In 141 periods (88% of total), for which video was available, 65% of alarms were deemed effective (followed by an alarm-related action within 10 minutes). Nurses were the sole responders in 55% of effective alarms and the patient or parent in 20%. Episodes of desaturation (SpO2 < 90%) were observed in 9 patients: At the time of the SpO2 paging trigger, the RR was >10 bpm in 6 of 9 patients. Based on all RR samples observed, the default alarm thresholds, to serve as a starting point for each patient, would be a low RR of 6 (>10 years of age) and 10 (4-9 years of age). CONCLUSIONS: In this study, the use of RR monitoring did not improve the detection of respiratory depression. An RR threshold, which would have been predictive of desaturations, would have resulted in an unacceptably high false alarm rate. Future research using a combination of variables (e.g., SpO2 and RR), or the measurement of tidal volumes, may be needed to improve patient safety in the postoperative ward.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".